A black hole is a harmful node in a graph that destroys any agent entering it, making its identification a critical task. In the \emph{Black Hole Search with Verification (BHSV)} problem, a team of agents operates on a graph $G$ with the objective that at least one agent survives and correctly identifies an edge incident to the black hole; if no black hole exists, then all agents must terminate. Prior work has studied BHS in arbitrary dynamic graphs under the restrictive \emph{face-to-face} communication model, where agents can exchange information only when co-located. This constraint significantly increases the number of agents required to solve the problem. In this work, we strengthen the capabilities of agents by equipping them with (i) \emph{1-hop visibility}, (ii) \emph{global communication}, and (iii) both \emph{1-hop visibility} and \emph{global communication}. We show that these enhancements lead to more efficient solutions for the BHSV problem in dynamic graphs.

翻译：黑洞是图中一种有害节点，会摧毁任何进入其中的智能体，因此识别黑洞成为一项关键任务。在《黑洞搜索与验证（BHSV）》问题中，智能体团队在图$G$上运行，目标是至少有一个智能体存活并正确识别与黑洞相连的边；若不存在黑洞，则所有智能体必须终止。先前的研究在限制性的\emph{面对面}通信模型下探讨了任意动态图中的黑洞搜索问题，该模型下智能体仅能在同一位置时交换信息。这一约束显著增加了解决问题所需的智能体数量。在本工作中，我们通过为智能体配备以下能力来增强其性能：(i) \emph{1跳可见性}，(ii) \emph{全局通信}，以及(iii) \emph{1跳可见性}与\emph{全局通信}的组合。我们证明，这些增强能力为动态图中的BHSV问题带来了更高效的解决方案。